Three-phase induction motors have been widely used in industrial drives as they are rugged, reliable, and economical. Variable-frequency drives (VFDs) are becoming more popular in variable-speed services because VFDs offer especially important energy-saving opportunities. Thus, many researchers have studied VFD-based control strategies for high performance of induction motor drives. One of these popular methods is the vector control method. Vector control of the induction motor can guarantee the decoupling of torque and flux control and thus control the induction motor linearly as a separated excited dc motor to offer better dynamic response. However, recent studies indicated that the conventional standard vector control strategy has a competing control nature in its current control loop so that true decoupled d- and q-axis loop control does not exist. As a result, the control performance is more sensitive to uncertainties such as unpredictable parameter variation, external load disturbances, and so on.
Therefore, it is important to investigate an effective and intelligent control scheme for enhanced induction motor drive performance. Most intelligent control research works focus on speed loop control based on neural networks. Peng and Zhao (K. Peng and J. Zhao, “Speed control of induction motor using neural network sliding mode controller,” in Proc. Int. Conf. Electr. Inf. Control Eng., Wuhan, China, April 2011, pp. 6125-6129 proposed to use a radial basis function neural network in the speed loop to provide better speed control performance, which can avoid the limit calculation under the uncertainties in the induction motor. Ben-Brahim and Kurosawa (L. Ben-Brahim and R. Kurosawa, “Identification of induction motor speed using neural networks,” in Proc. Power Conyers. Conf., Yokohama, Japan, April 1993, pp. 689-694) used a backpropagation neural network to identify the mechanical speed of an induction motor and use neural network speed estimator to better control motor speed based on the conventional vector control method. However, the competing control nature associated with the current loop is not solved, and thus, the improvement only in speed loop does not help to achieve the true decoupled control. Some research proposed a neurocontroller for the current loop control of the induction motor used a Lyapunov-based online learning algorithm and showed its benefit, but the computational requirement of online learning is a big burden. (See, for example, J. Restrepo, J. Viola, R. G. Harley, and T. G. Habetler, “Induction machine current loop neurocontroller employing a Lyapunov based training algorithm,” in Proc. IEEE Power Eng. Soc. Gen. Meeting, Tampa, Fla., USA, April 2003, pp. 1-8; or J. Restrepo, B. Burton, R. G. Harley, and T. G. Habetler, “ANN based current control of a VSI fed AC machine using line coordinates,” in Proc. 5th IEEE Int. Caracas Conf. Devices Circuits Syst., November 2004, pp. 225-229.)
In recent years, significant research work has been conducted in the area of dynamic programming (DP) for optimal control of nonlinear systems. Adaptive critic designs constitute a class of approximate dynamic programming (ADP) methods that use incremental optimization techniques combined with parametric structures that approximate the optimal cost and the control of a system. In G. K. Venayagamoorthy, R. G. Harley, and D. C. Wunsch, “Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator,” IEEE Trans. Neural Netw., vol. 13, no. 3, pp. 764-773, May 2002, both heuristic dynamic programming and dual heuristic programming have been used to control a turbogenerator. In S. Li, M. Fairbank, D. C. Wunsch, and E. Alonso, “Vector control of a grid-connected rectifier/inverter using an artificial neural network,” in Proc. IEEE World Congr. Comput. Intell., Brisbane, Australia, June 2012, pp. 1-7, an ADP-based neural network (NN) controller is trained and used to control a grid-connected converter system, which demonstrated an excellent performance compared to the conventional standard vector controller.
However, no known previous work discloses a current loop vector controller for a three-phase induction motor using an ADP-based NN. Therefore, what is desired are improved control systems for controlling three-phase induction motors. In particular, systems, methods and devices are desired for controlling three-phase induction motors using an ADP-based neural network.